This work introduces a simple deep-learning based method to delineate contours by `walking' along learnt unit vector fields. We demonstrate the effectiveness of our pipeline on the unique case of open contours on the task of delineating the sacroiliac joints (SIJs) in spinal MRIs. We show that: (i) 95% of the time the average root mean square error of the predicted contour against the original ground truth is below 4.5 pixels (2.5mm for a standard T1-weighted SIJ MRI), and (ii) the proposed method is better than the baseline of regressing vertices or landmarks of contours.

翻译：本文提出了一种基于深度学习的简单方法，通过沿学习到的单位向量场进行“行走”来描绘轮廓。我们在脊柱MRI中骶髂关节（SIJ）描绘这一开放轮廓的特殊任务上验证了该流程的有效性。研究结果表明：（i）在95%的情况下，预测轮廓与原始真实标注的平均均方根误差低于4.5个像素（对于标准T1加权SIJ MRI相当于2.5毫米）；（ii）所提出的方法优于通过回归轮廓顶点或关键点作为基线的方法。